library(ggplot2)
library(grid)
library(forestploter)
library(tidyverse)
saveto <- "/Volumes/Research Project/IAT_Retest/Plotting/SavedFigs/"
path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))
Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Estimates/UCR inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
Cal1 <-read.csv("IndividualParametersQuadUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersQuadUCR2.csv", header = T)

colnames(Cal1)[2:ncol(Cal1)]<-paste(colnames(Cal1[2:ncol(Cal1)]),"T1",sep="_")
colnames(Cal2)[2:ncol(Cal2)]<-paste(colnames(Cal2[2:ncol(Cal2)]),"T2",sep="_")

Q.Cal <- merge(Cal1, Cal2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersQuadProjectImplicit1.csv")
PI2 <- read.csv("IndividualParametersQuadProjectImplicit2.csv")

colnames(PI1)[2:ncol(PI1)]<-paste(colnames(PI1[2:ncol(PI1)]),"T1",sep="_")
colnames(PI2)[2:ncol(PI2)]<-paste(colnames(PI2[2:ncol(PI2)]),"T2",sep="_")

Q.PI <- merge(PI1, PI2, by = "ID", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersQuadGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersQuadGawronski2.csv")

colnames(Gaw1)[2:ncol(Gaw1)]<-paste(colnames(Gaw1[2:ncol(Gaw1)]),"T1",sep="_")
colnames(Gaw2)[2:ncol(Gaw2)]<-paste(colnames(Gaw2[2:ncol(Gaw2)]),"T2",sep="_")

Q.Gaw <- merge(Gaw1, Gaw2, by = "code", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersQuadForscher1.csv", header = T)
For2<-read.csv("IndividualParametersQuadForscher2.csv", header = T)

colnames(For1)[2:ncol(For1)]<-paste(colnames(For1[2:ncol(For1)]),"T1",sep="_")
colnames(For2)[2:ncol(For2)]<-paste(colnames(For2[2:ncol(For2)]),"T2",sep="_")

Q.For <- merge(For1, For2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1Quad1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1Quad2.csv", header = T)

colnames(Lai11)[2:ncol(Lai11)]<-paste(colnames(Lai11[2:ncol(Lai11)]),"T1",sep="_")
colnames(Lai12)[2:ncol(Lai12)]<-paste(colnames(Lai12[2:ncol(Lai12)]),"T2",sep="_")

Q.Lai1 <- merge(Lai11, Lai12, by = "session_id", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2Quad1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2Quad2.csv", header = T)

colnames(Lai21)[2:ncol(Lai21)]<-paste(colnames(Lai21[2:ncol(Lai21)]),"T1",sep="_")
colnames(Lai22)[2:ncol(Lai22)]<-paste(colnames(Lai22[2:ncol(Lai22)]),"T2",sep="_")

Q.Lai2 <- merge(Lai21, Lai22, by = "session_id", all.x = T, all.y = T)

colnames(Q.Cal)[1] <- "subID"
Q.Cal <- cbind(Q.Cal, Study = "Calanchini")
colnames(Q.PI)[1] <- "subID"
Q.PI <- cbind(Q.PI, Study = "PI")
colnames(Q.Gaw)[1] <- "subID"
Q.Gaw <- cbind(Q.Gaw, Study = "Gawronski")
colnames(Q.For)[1] <- "subID"
Q.For <- cbind(Q.For, Study = "Forscher")
colnames(Q.Lai1)[1] <- "subID"
Q.Lai1 <- cbind(Q.Lai1, Study = "Lai 1")
colnames(Q.Lai2)[1] <- "subID"
Q.Lai2 <- cbind(Q.Lai2, Study = "Lai 2")

Quad <- rbind(Q.Cal,Q.PI,Q.Gaw,Q.For,Q.Lai1,Q.Lai2)


Quad$Study[Quad$Study=="PI"]<-"Project Implicit, 2020"
Quad$Study[Quad$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
Quad$Study[Quad$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
Quad$Study[Quad$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
Quad$Study[Quad$Study=="Gawronski"]<-"Gawronski et al., 2017"
Quad$Study[Quad$Study=="Forscher"]<-"Forscher et al., 2017"
path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))
Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Estimates/UCR inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
Cal1 <-read.csv("IndividualParametersPDUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersPDUCR2.csv", header = T)

colnames(Cal1)[2:ncol(Cal1)]<-paste(colnames(Cal1[2:ncol(Cal1)]),"T1",sep="_")
colnames(Cal2)[2:ncol(Cal2)]<-paste(colnames(Cal2[2:ncol(Cal2)]),"T2",sep="_")

P.Cal <- merge(Cal1, Cal2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersProjectImplicitPD1.csv")
PI2 <- read.csv("IndividualParametersProjectImplicitPD2.csv")

colnames(PI1)[2:ncol(PI1)]<-paste(colnames(PI1[2:ncol(PI1)]),"T1",sep="_")
colnames(PI2)[2:ncol(PI2)]<-paste(colnames(PI2[2:ncol(PI2)]),"T2",sep="_")

P.PI <- merge(PI1, PI2, by = "ID", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersPDGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersPDGawronski2.csv")

colnames(Gaw1)[2:ncol(Gaw1)]<-paste(colnames(Gaw1[2:ncol(Gaw1)]),"T1",sep="_")
colnames(Gaw2)[2:ncol(Gaw2)]<-paste(colnames(Gaw2[2:ncol(Gaw2)]),"T2",sep="_")

P.Gaw <- merge(Gaw1, Gaw2, by = "code", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersForscherPD1.csv", header = T)
For2<-read.csv("IndividualParametersForscherPD2.csv", header = T)

colnames(For1)[2:ncol(For1)]<-paste(colnames(For1[2:ncol(For1)]),"T1",sep="_")
colnames(For2)[2:ncol(For2)]<-paste(colnames(For2[2:ncol(For2)]),"T2",sep="_")

P.For <- merge(For1, For2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1PD1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1PD2.csv", header = T)

colnames(Lai11)[2:ncol(Lai11)]<-paste(colnames(Lai11[2:ncol(Lai11)]),"T1",sep="_")
colnames(Lai12)[2:ncol(Lai12)]<-paste(colnames(Lai12[2:ncol(Lai12)]),"T2",sep="_")

P.Lai1 <- merge(Lai11, Lai12, by = "session_id", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2PD1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2PD2.csv", header = T)

colnames(Lai21)[2:ncol(Lai21)]<-paste(colnames(Lai21[2:ncol(Lai21)]),"T1",sep="_")
colnames(Lai22)[2:ncol(Lai22)]<-paste(colnames(Lai22[2:ncol(Lai22)]),"T2",sep="_")

P.Lai2 <- merge(Lai21, Lai22, by = "session_id", all.x = T, all.y = T)

colnames(P.Cal)[1] <- "subID"
P.Cal <- cbind(P.Cal, Study = "Calanchini")
colnames(P.PI)[1] <- "subID"
P.PI <- cbind(P.PI, Study = "PI")
colnames(P.Gaw)[1] <- "subID"
P.Gaw <- cbind(P.Gaw, Study = "Gawronski")
colnames(P.For)[1] <- "subID"
P.For <- cbind(P.For, Study = "Forscher")
colnames(P.Lai1)[1] <- "subID"
P.Lai1 <- cbind(P.Lai1, Study = "Lai 1")
colnames(P.Lai2)[1] <- "subID"
P.Lai2 <- cbind(P.Lai2, Study = "Lai 2")

PD <- rbind(P.Cal,P.PI,P.Gaw,P.For,P.Lai1,P.Lai2)

PD$Study[PD$Study=="PI"]<-"Project Implicit, 2020"
PD$Study[PD$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
PD$Study[PD$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
PD$Study[PD$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
PD$Study[PD$Study=="Gawronski"]<-"Gawronski et al., 2017"
PD$Study[PD$Study=="Forscher"]<-"Forscher et al., 2017"

Quad Model

Associate Black-Bad

ACbbScatter <- ggplot(Quad, aes(x=ACbb1_T1, y=ACbb1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association Black-Bad",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa() 
ACbbScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"ACbbScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"ACbbScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Associate White-Good

ACwgScatter <- ggplot(Quad, aes(x=ACwg1_T1, y=ACwg1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association White-Good",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) + theme(legend.title = element_text(size = 9)) +
  jtools::theme_apa()
ACwgScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"ACwgScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"ACwgScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Detection

DScatter <- ggplot(Quad, aes(x=D1_T1, y=D1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Detection",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) + theme(legend.title = element_text(size = 9)) +
  jtools::theme_apa()
DScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"DScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"DScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Overcoming Bias

OBScatter <- ggplot(Quad, aes(x=OB1_T1, y=OB1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Overcoming Bias",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) + theme(legend.title = element_text(size = 9))  +
  jtools::theme_apa()
OBScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"OBScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"OBScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Guessing

GScatter <- ggplot(Quad, aes(x=G1_T1, y=G1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Guessing",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
ggsave(paste0(saveto,"GScatter.jpg"),dpi=600)
Saving 7 x 7 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"GScatter.tiff"),dpi=600)
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`geom_smooth()` using formula 'y ~ x'

Panel

library(ggpubr)
QuadPanel1 <- ggarrange(ACwgScatter, ACbbScatter, DScatter, OBScatter, GScatter,
          ncol = 3, nrow = 2, common.legend = T)
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
QuadPanel1


require(grid)   # for the textGrob() function

QuadPanel2 <- ggarrange(ACwgScatter  + theme(plot.title = element_text(size=12)) + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        ACbbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        DScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        OBScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        GScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = "bottom",
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
QuadPanel2


annotate_figure(QuadPanel2, left = textGrob("Time 2", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Time 1", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
ggsave(paste0(saveto,"QuadPanel.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
ggsave(paste0(saveto,"QuadPanel.tiff"),dpi=600)
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PD Model

Automatic Black

AbScatter <- ggplot(PD, aes(x=Ab1_T1, y=Ab1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic Black",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
AbScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"AbScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"AbScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Associate White

AwScatter <- ggplot(PD, aes(x=Aw1_T1, y=Aw1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic White",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
AwScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"AwScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"AwScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Control Good

CpScatter <- ggplot(PD, aes(x=Cp1_T1, y=Cp1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Good",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CpScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CpScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CpScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Control Bad

CuScatter <- ggplot(PD, aes(x=Cu1_T1, y=Cu1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Bad",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CuScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CuScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CuScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Control White

CwScatter <- ggplot(PD, aes(x=Cw1_T1, y=Cw1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control White",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CwScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CwScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CwScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Control Black

CbScatter <- ggplot(PD, aes(x=Cb1_T1, y=Cb1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Black",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CbScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CbScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CbScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Panel

library(ggpubr)
PDPanel1 <- ggarrange(AwScatter, AbScatter, CuScatter, CpScatter, CbScatter, CwScatter,
          ncol = 3, nrow = 2, common.legend = T)
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
PDPanel1


require(grid)   # for the textGrob() function

PDPanel2 <- ggarrange(AwScatter  + theme(plot.title = element_text(size=12))  + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        AbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CuScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CpScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CwScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = "bottom",
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
PDPanel2 <- annotate_figure(PDPanel2, left = textGrob("Time 2", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Time 1", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
PDPanel2
ggsave(paste0(saveto,"PDPanel.jpg"),dpi=600)
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ggsave(paste0(saveto,"PDPanel.tiff"),dpi=600)
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Reliability Meta-Analysis

dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Retest/output/combQuad_Df.csv")[2:6]

dt$Param[dt$Param=="D"]<-"Detection"
dt$Param[dt$Param=="OB"]<-"Overcoming Bias"
dt$Param[dt$Param=="ACbb"]<-"Association Black-Bad"
dt$Param[dt$Param=="ACwg"]<-"Association White-Good"
dt$Param[dt$Param=="G"]<-"Guessing"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

colnames(dt)[colnames(dt)=="Param"] <- "   "
dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`ICC (95% CI)` <- ifelse(is.na(dt$ICC), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$ICC, dt$CI.LB, dt$CI.UB))


dt$ICCstudy <- ifelse(dt$type==1, dt$ICC, NA)
dt$ICC.LBstudy <- ifelse(dt$type==1, dt$CI.LB, NA)
dt$ICC.UBstudy <- ifelse(dt$type==1, dt$CI.UB, NA)
dt$Sizestudy <- ifelse(dt$type==1, .6, NA)
dt$Sizemeta <- ifelse(dt$type==0, 1.2, NA)
dt$ICCmeta <- ifelse(dt$type==0, dt$ICC, NA)
dt$ICC.LBmeta <- ifelse(dt$type==0, dt$CI.LB, NA)
dt$ICC.UBmeta <- ifelse(dt$type==0, dt$CI.UB, NA)

#dt<-dt[c(1:4,6:7)]
dt<-dt[c(1:4,6:ncol(dt))]
colnames(dt)[colnames(dt)=="ICC (95% CI)"] <- "     "

library(forestploter)
# p <- forest(
#   dt[c(1,5,6)],
#             est = dt$ICC,
#             lower = dt$CI.LB, 
#             upper = dt$CI.UB,
#             ci_column = 2,
#             ref_line = .4,
#             xlim = c(-.1, 1),
#             ticks_at = c(0, .4,.6, .75, 1),
#   sizes = .1
# )

tm <- forest_theme(
                   ci_pch = c(15, 16),
                   ci_col = c("black","red"),
                   ci_lty = c( "solid", "dashed"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Study")
                  
                  )

p <- forest(
  dt[c(1,5,6)],
            est = list(dt$ICCmeta, dt$ICCstudy),
            lower = list(dt$ICC.LBmeta, dt$ICC.LBstudy), 
            upper = list(dt$ICC.UBmeta, dt$ICC.UBstudy),
              ci_column = 2,
            ref_line = .4,
            xlim = c(-.1, 1),
            ticks_at = c(0, .4,.6, .75, 1),
  sizes = list(dt$Sizemeta, dt$Sizestudy),
  theme = tm
)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt),seq(from=1,to=nrow(dt),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))
png(paste0(saveto,"Quad_TRT.png"), units="in", width=10, height=12, res=300)
plot(p)
dev.off()
null device 
          1 
# ggsave function
ggplot2::ggsave(paste0(saveto,"Quad_TRT.png"),
                dpi = 300,
                width = 6, height = 11, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"Quad_TRT.tiff"),
                dpi = 600,
                width = 6, height = 11, units = "in", plot=p)
dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Retest/output/combPD_Df.csv")[2:6]

dt$Param[dt$Param=="Ab"]<-"Automatic Black"
dt$Param[dt$Param=="Aw"]<-"Automatic White"
dt$Param[dt$Param=="Cb"]<-"Control Black"
dt$Param[dt$Param=="Cw"]<-"Control White"
dt$Param[dt$Param=="Cu"]<-"Control Bad"
dt$Param[dt$Param=="Cp"]<-"Control Good"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

colnames(dt)[colnames(dt)=="Param"] <- "   "
colnames(dt)[colnames(dt)=="Param"] <- "   "
dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`ICC (95% CI)` <- ifelse(is.na(dt$ICC), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$ICC, dt$CI.LB, dt$CI.UB))

dt$ICCstudy <- ifelse(dt$type==1, dt$ICC, NA)
dt$ICC.LBstudy <- ifelse(dt$type==1, dt$CI.LB, NA)
dt$ICC.UBstudy <- ifelse(dt$type==1, dt$CI.UB, NA)
dt$Sizestudy <- ifelse(dt$type==1, .6, NA)
dt$Sizemeta <- ifelse(dt$type==0, 1.2, NA)
dt$ICCmeta <- ifelse(dt$type==0, dt$ICC, NA)
dt$ICC.LBmeta <- ifelse(dt$type==0, dt$CI.LB, NA)
dt$ICC.UBmeta <- ifelse(dt$type==0, dt$CI.UB, NA)

#dt<-dt[c(1:4,6:7)]
dt<-dt[c(1:4,6:ncol(dt))]
colnames(dt)[colnames(dt)=="ICC (95% CI)"] <- "     "

library(forestploter)
# p <- forest(
#   dt[c(1,5,6)],
#             est = dt$ICC,
#             lower = dt$CI.LB, 
#             upper = dt$CI.UB,
#             ci_column = 2,
#             ref_line = .4,
#             xlim = c(-.1, 1),
#             ticks_at = c(0, .4,.6, .75, 1),
#   sizes = .1
# )

tm <- forest_theme(
                   ci_pch = c(15, 16),
                   ci_col = c("black","red"),
                   ci_lty = c( "solid", "dashed"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Study")
                  
                  )

p <- forest(
  dt[c(1,5,6)],
            est = list(dt$ICCmeta, dt$ICCstudy),
            lower = list(dt$ICC.LBmeta, dt$ICC.LBstudy), 
            upper = list(dt$ICC.UBmeta, dt$ICC.UBstudy),
              ci_column = 2,
            ref_line = .4,
            xlim = c(-.1, 1),
            ticks_at = c(0, .4,.6, .75, 1),
  sizes = list(dt$Sizemeta, dt$Sizestudy),
  theme = tm
)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt),seq(from=1,to=nrow(dt),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))
png(paste0(saveto,"PDP_TRT.png"), units="in", width=10, height=15, res=300)
plot(p)
dev.off()
null device 
          1 
# ggsave function
ggplot2::ggsave(paste0(saveto,"PDP_TRT.png"),
                dpi = 300,
                width = 6, height = 13, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"PDP_TRT.tiff"),
                dpi = 600,
                width = 6, height = 13, units = "in", plot=p)

Recovery

setwd(paste0("/Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/PD/"))
Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/PD inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
times<-c("Time1","Time2")

allrecovs <- as.data.frame(matrix(ncol=14,nrow=0))

PDdirs <- list.dirs(path = ".", full.names = TRUE, recursive = TRUE)[-1]
for(d in PDdirs){
  for(ti in times){
    curFiles <- list.files(path=d,pattern="\\.csv$")
    id<-grep(ti,curFiles)
    output<-read.csv(paste0(d,"/",curFiles[id]))
    name<-gsub("./","",d)
    name<-gsub(" ","",name)
    
    if(ti=="Time1"){
      #colnames(output) <- paste(colnames(output),"T1",sep="_")
      output$time <- "T1"
    }else if(ti=="Time2"){
      #colnames(output) <- paste(colnames(output),"T2",sep="_")
      output$time <- "T2"
    }
    
    assign(paste0(name,".PD.Recov.",ti),output)
  }
  df <- rbind(get(paste0(name,".PD.Recov.Time1")),get(paste0(name,".PD.Recov.Time2")))
  assign(paste0(name,".PD.Recov"),df)
  allrecovs <- rbind(allrecovs,cbind(df,Study=name))
}


allrecovs$Study <- gsub("Lai1", "Lai 1", allrecovs$Study)
allrecovs$Study <- gsub("Lai2", "Lai 2", allrecovs$Study)
PD.recovs <- allrecovs
PD.recovs <- dplyr::rename(PD.recovs, subID=X)

tapply(PD.recovs$Ab1, PD.recovs$Study, function(x) length(x))
Calanchini   Forscher  Gawronski      Lai 1      Lai 2         PI 
       210         64        232        160        926       2480 
path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))
Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Estimates/UCR inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
Cal1 <-read.csv("IndividualParametersPDUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersPDUCR2.csv", header = T)

Cal1$time <- "T1"
Cal2$time <- "T2"
Cal1$subID <- 1:nrow(Cal1)
Cal2$subID <- 1:nrow(Cal2)
P.Cal <- rbind(Cal1, Cal2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersProjectImplicitPD1.csv")
PI2 <- read.csv("IndividualParametersProjectImplicitPD2.csv")

PI1$time <- "T1"
PI2$time <- "T2"
PI1$subID <- 1:nrow(PI1)
PI2$subID <- 1:nrow(PI2)
P.PI <- rbind(PI1, PI2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersPDGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersPDGawronski2.csv")

Gaw1$time <- "T1"
Gaw2$time <- "T2"
Gaw1$subID <- 1:nrow(Gaw1)
Gaw2$subID <- 1:nrow(Gaw2)
P.Gaw <- rbind(Gaw1, Gaw2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersForscherPD1.csv", header = T)
For2<-read.csv("IndividualParametersForscherPD2.csv", header = T)

For1$time <- "T1"
For2$time <- "T2"
For1$subID <- 1:nrow(For1)
For2$subID <- 1:nrow(For2)
P.For <- rbind(For1, For2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1PD1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1PD2.csv", header = T)

Lai11$time <- "T1"
Lai12$time <- "T2"
Lai11$subID <- 1:nrow(Lai11)
Lai12$subID <- 1:nrow(Lai12)
P.Lai1 <- rbind(Lai11, Lai12)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2PD1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2PD2.csv", header = T)

Lai21$time <- "T1"
Lai22$time <- "T2"
Lai21$subID <- 1:nrow(Lai21)
Lai22$subID <- 1:nrow(Lai22)
P.Lai2 <- rbind(Lai21,Lai22)

colnames(P.Cal)[1] <- "subID"
P.Cal <- cbind(P.Cal, Study = "Calanchini")
colnames(P.PI)[1] <- "subID"
P.PI <- cbind(P.PI, Study = "PI")
colnames(P.Gaw)[1] <- "subID"
P.Gaw <- cbind(P.Gaw, Study = "Gawronski")
colnames(P.For)[1] <- "subID"
P.For <- cbind(P.For, Study = "Forscher")
colnames(P.Lai1)[1] <- "subID"
P.Lai1 <- cbind(P.Lai1, Study = "Lai 1")
colnames(P.Lai2)[1] <- "subID"
P.Lai2 <- cbind(P.Lai2, Study = "Lai 2")

PD.orig <- rbind(P.Cal,P.PI,P.Gaw,P.For,P.Lai1,P.Lai2)
PD.orig[1] <- NULL

colnames(PD.recovs)[2: (ncol(PD.recovs)-2) ] <- paste(colnames(PD.recovs)[2: (ncol(PD.recovs)-2) ],"R",sep="_")
colnames(PD.orig)[1: (ncol(PD.orig)-3) ] <- paste(colnames(PD.orig)[1: (ncol(PD.orig)-3) ],"O",sep="_")
PD.orig <- PD.orig[order(PD.orig$Study, PD.orig$time),]
PD.recovs <- PD.recovs[order(PD.recovs$Study, PD.recovs$time),]

#PD.orig.recovs <- cbind(PD.orig,PD.recovs)
PD.orig.recovs <- merge(PD.orig, PD.recovs, by = c("subID","Study","time"))

PD.orig.recovs$Study[PD.orig.recovs$Study=="PI"]<-"Project Implicit, 2020"
PD.orig.recovs$Study[PD.orig.recovs$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
PD.orig.recovs$Study[PD.orig.recovs$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
PD.orig.recovs$Study[PD.orig.recovs$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
PD.orig.recovs$Study[PD.orig.recovs$Study=="Gawronski"]<-"Gawronski et al., 2017"
PD.orig.recovs$Study[PD.orig.recovs$Study=="Forscher"]<-"Forscher et al., 2017"

Recovery

PD Model

Automatic Black

AbScatter <- ggplot(PD.orig.recovs, aes(x=Ab1_O, y=Ab1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype = "dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic Black",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()

AbScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"AbScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"AbScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Automatic White

AwScatter <- ggplot(PD.orig.recovs, aes(x=Aw1_O, y=Aw1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic White",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
AwScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"AwScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"AwScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Control Good

CpScatter <- ggplot(PD.orig.recovs, aes(x=Cp1_O, y=Cp1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Good",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CpScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CpScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"AwScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Control Bad

CuScatter <- ggplot(PD.orig.recovs, aes(x=Cu1_O, y=Cu1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Bad",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CuScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CuScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CuScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Control White

CwScatter <- ggplot(PD.orig.recovs, aes(x=Cw1_O, y=Cw1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control White",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CwScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CwScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CwScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Control Black

CbScatter <- ggplot(PD.orig.recovs, aes(x=Cb1_O, y=Cb1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Black",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CbScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CbScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"CbScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Panel

library(ggpubr)
PDPanel1 <- ggarrange(AwScatter, AbScatter, CuScatter, CpScatter, CbScatter, CwScatter,
          ncol = 3, nrow = 2, common.legend = T)
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
PDPanel1


require(grid)   # for the textGrob() function

PDPanel2 <- ggarrange(AwScatter  + theme(plot.title = element_text(size=12)) + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        AbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CuScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CpScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CwScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = c("bottom"),
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
PDPanel2 <- annotate_figure(PDPanel2, left = textGrob("Recovered", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Original", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
PDPanel2
ggsave(paste0(saveto,"PDPanelREC.jpg"),dpi=600)
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ggsave(paste0(saveto,"PDPanelREC.tiff"),dpi=600)
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Quad Model

Recovery

setwd(paste0("/Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/Quad/"))
Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/Quad inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
times<-c("Time1","Time2")

allrecovs <- as.data.frame(matrix(ncol=14,nrow=0))

Quaddirs <- list.dirs(path = ".", full.names = TRUE, recursive = TRUE)[-1]
for(d in Quaddirs){
  for(ti in times){
    curFiles <- list.files(path=d,pattern="\\.csv$")
    id<-grep(ti,curFiles)
    output<-read.csv(paste0(d,"/",curFiles[id]))
    name<-gsub("./","",d)
    name<-gsub(" ","",name)
    
    if(ti=="Time1"){
      #colnames(output) <- paste(colnames(output),"T1",sep="_")
      output$time <- "T1"
    }else if(ti=="Time2"){
      #colnames(output) <- paste(colnames(output),"T2",sep="_")
      output$time <- "T2"
    }
    
    assign(paste0(name,".Quad.Recov.",ti),output)
  }
  df <- rbind(get(paste0(name,".Quad.Recov.Time1")),get(paste0(name,".Quad.Recov.Time2")))
  assign(paste0(name,".Quad.Recov"),df)
  allrecovs <- rbind(allrecovs,cbind(df,Study=name))
}


allrecovs$Study <- gsub("Lai1", "Lai 1", allrecovs$Study)
allrecovs$Study <- gsub("Lai2", "Lai 2", allrecovs$Study)
Quad.recovs <- allrecovs
Quad.recovs <- dplyr::rename(Quad.recovs, subID=X)

tapply(Quad.recovs$ACbb1, Quad.recovs$Study, function(x) length(x))
Calanchini   Forscher  Gawronski      Lai 1      Lai 2         PI 
       210         64        232        160        926       2480 
path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))
Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Estimates/UCR inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
Cal1 <-read.csv("IndividualParametersQuadUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersQuadUCR2.csv", header = T)

Cal1$time <- "T1"
Cal2$time <- "T2"
Cal1$subID <- 1:nrow(Cal1)
Cal2$subID <- 1:nrow(Cal2)
P.Cal <- rbind(Cal1, Cal2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersQuadProjectImplicit1.csv")
PI2 <- read.csv("IndividualParametersQuadProjectImplicit2.csv")

PI1$time <- "T1"
PI2$time <- "T2"
PI1$subID <- 1:nrow(PI1)
PI2$subID <- 1:nrow(PI2)
P.PI <- rbind(PI1, PI2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersQuadGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersQuadGawronski2.csv")

Gaw1$time <- "T1"
Gaw2$time <- "T2"
Gaw1$subID <- 1:nrow(Gaw1)
Gaw2$subID <- 1:nrow(Gaw2)
P.Gaw <- rbind(Gaw1, Gaw2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersQuadForscher1.csv", header = T)
For2<-read.csv("IndividualParametersQuadForscher2.csv", header = T)

For1$time <- "T1"
For2$time <- "T2"
For1$subID <- 1:nrow(For1)
For2$subID <- 1:nrow(For2)
P.For <- rbind(For1, For2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1Quad1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1Quad2.csv", header = T)

Lai11$time <- "T1"
Lai12$time <- "T2"
Lai11$subID <- 1:nrow(Lai11)
Lai12$subID <- 1:nrow(Lai12)
P.Lai1 <- rbind(Lai11, Lai12)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2Quad1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2Quad2.csv", header = T)

Lai21$time <- "T1"
Lai22$time <- "T2"
Lai21$subID <- 1:nrow(Lai21)
Lai22$subID <- 1:nrow(Lai22)
P.Lai2 <- rbind(Lai21,Lai22)

colnames(P.Cal)[1] <- "subID"
P.Cal <- cbind(P.Cal, Study = "Calanchini")
colnames(P.PI)[1] <- "subID"
P.PI <- cbind(P.PI, Study = "PI")
colnames(P.Gaw)[1] <- "subID"
P.Gaw <- cbind(P.Gaw, Study = "Gawronski")
colnames(P.For)[1] <- "subID"
P.For <- cbind(P.For, Study = "Forscher")
colnames(P.Lai1)[1] <- "subID"
P.Lai1 <- cbind(P.Lai1, Study = "Lai 1")
colnames(P.Lai2)[1] <- "subID"
P.Lai2 <- cbind(P.Lai2, Study = "Lai 2")

Quad.orig <- rbind(P.Cal,P.PI,P.Gaw,P.For,P.Lai1,P.Lai2)
Quad.orig[1] <- NULL
colnames(Quad.recovs)[2: (ncol(Quad.recovs)-2) ] <- paste(colnames(Quad.recovs)[2: (ncol(Quad.recovs)-2) ],"R",sep="_")
colnames(Quad.orig)[1: (ncol(Quad.orig)-3) ] <- paste(colnames(Quad.orig)[1: (ncol(Quad.orig)-3) ],"O",sep="_")
Quad.orig <- Quad.orig[order(Quad.orig$Study, Quad.orig$time),]
Quad.recovs <- Quad.recovs[order(Quad.recovs$Study, Quad.recovs$time),]

#Quad.orig.recovs <- cbind(Quad.orig,Quad.recovs)
Quad.orig.recovs <- merge(Quad.orig, Quad.recovs, by = c("subID","Study","time"))

Quad.orig.recovs$Study[Quad.orig.recovs$Study=="PI"]<-"Project Implicit, 2020"
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Gawronski"]<-"Gawronski et al., 2017"
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Forscher"]<-"Forscher et al., 2017"

Quad Model

Associate Black-Bad

ACbbScatter <- ggplot(Quad.orig.recovs, aes(x=ACbb1_O, y=ACbb1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association Black-Bad",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
ACbbScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"ACbbScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"ACbbScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Associate White-Good

ACwgScatter <- ggplot(Quad.orig.recovs, aes(x=ACwg1_O, y=ACwg1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association White-Good",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
ACwgScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"ACwgScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"ACwgScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Detection

DScatter <- ggplot(Quad.orig.recovs, aes(x=D1_O, y=D1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Detection",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
DScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"DScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"DScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Overcoming Bias

OBScatter <- ggplot(Quad.orig.recovs, aes(x=OB1_O, y=OB1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Overcoming Bias",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
OBScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"OBScatterREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"OBScatterREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Guessing

GScatter <- ggplot(Quad.orig.recovs, aes(x=G1_O, y=G1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Guessing",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
GScatter
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"GScatter.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'
ggsave(paste0(saveto,"GScatter.tiff"),dpi=600)
Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Panel

library(ggpubr)
QuadPanel1 <- ggarrange(ACwgScatter, ACbbScatter, DScatter, OBScatter, GScatter,
          ncol = 3, nrow = 2, common.legend = T)
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
QuadPanel1


require(grid)   # for the textGrob() function

QuadPanel2 <- ggarrange(ACwgScatter  + theme(plot.title = element_text(size=12))  + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        ACbbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        DScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        OBScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        GScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = "bottom",
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
QuadPanel2


annotate_figure(QuadPanel2, left = textGrob("Recovered", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Original", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
ggsave(paste0(saveto,"QuadPanelREC.jpg"),dpi=600)
Saving 7.29 x 4.51 in image
ggsave(paste0(saveto,"QuadPanelREC.tiff"),dpi=600)
Saving 7.29 x 4.51 in image

Recovery Meta-Analysis

Quad

dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Recovery/output/combQuad_Recovery_Df.csv")[2:7]
dt$id <- unlist(lapply(1:5, function(x) rep(x,times=13)))

dt$Param[dt$Param=="D"]<-"Detection"
dt$Param[dt$Param=="OB"]<-"Overcoming Bias"
dt$Param[dt$Param=="ACbb"]<-"Association Black-Bad"
dt$Param[dt$Param=="ACwg"]<-"Association White-Good"
dt$Param[dt$Param=="G"]<-"Guessing"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`Recovery (95% CI)` <- ifelse(is.na(dt$Recov), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$Recov, dt$CI.LB, dt$CI.UB))

dt2 <- pivot_wider(dt, id_cols = c("Param","id"),  names_from = c(Time), values_from = c(Recov,CI.LB,CI.UB,`Recovery (95% CI)`) )
dt2$`Recovery [95% CI]` <- ifelse(!is.na(dt2$`Recovery (95% CI)_Meta`), paste0("                 ",dt2$`Recovery (95% CI)_Meta`), paste0("T1 :", paste0(dt2$`Recovery (95% CI)_Time1`), " ; ", "T2: ", paste0(dt2$`Recovery (95% CI)_Time2`)))
dt2$` ` <- paste(rep(" ", 20), collapse = " ")


dt3<-dt2 %>% select(Param,Recov_Meta:11,15,16)

colnames(dt3)[colnames(dt3)=="Param"] <- "   "
colnames(dt3)[colnames(dt3)=="Recovery [95% CI]"] <- "    "

dt3$size_Meta <- ifelse(is.na(dt3$Recov_Meta), NA, 1.2)
dt3$size_T1 <- ifelse(is.na(dt3$Recov_Time1), NA, .6)
dt3$size_T2 <- ifelse(is.na(dt3$Recov_Time2), NA, .6)

# Set-up theme
tm <- forest_theme(
                   ci_pch = c(15, 16, 17),
                   ci_col = c("black","red","blue"),
                   ci_lty = c( "solid", "dashed", "dotted"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Time 1 ", "Time 2 "))

library(forestploter)
p <- forest(
  dt3[c(1,12,11)],
            est = list(dt3$Recov_Meta, dt3$Recov_Time1, dt3$Recov_Time2),
            lower = list(dt3$CI.LB_Meta, dt3$CI.LB_Time1, dt3$CI.LB_Time2), 
            upper = list(dt3$CI.UB_Meta, dt3$CI.UB_Time1, dt3$CI.UB_Time2),
            ci_column = 2,
            ref_line = .7,
            xlim = c(-.1, 1),
            ticks_at = c(0, .2, .5, .7, 1),
  sizes = list(dt3$size_Meta, dt3$size_T1, dt3$size_T2),
  theme = tm
)
plot(p)


library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt3),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt3),seq(from=1,to=nrow(dt3),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))
png("~/Desktop/Quad_RecovForest.png", units="in", width=10, height=15, res=300)
plot(p)
dev.off()
null device 
          1 
# ggsave function
ggplot2::ggsave(paste0(saveto,"Quad_RecovForest.png"),
                dpi = 300,
                width = 8, height = 15, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"Quad_RecovForest.tiff"),
                dpi = 300,
                width = 8, height = 15, units = "in", plot=p)
dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Recovery/output/combPD_Recovery_Df.csv")[2:7]
dt$id <- unlist(lapply(1:6, function(x) rep(x,times=13)))

dt$Param[dt$Param=="Ab"]<-"Automatic Black"
dt$Param[dt$Param=="Aw"]<-"Automatic White"
dt$Param[dt$Param=="Cb"]<-"Control Black"
dt$Param[dt$Param=="Cw"]<-"Control White"
dt$Param[dt$Param=="Cu"]<-"Control Bad"
dt$Param[dt$Param=="Cp"]<-"Control Good"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`Recovery (95% CI)` <- ifelse(is.na(dt$Recov), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$Recov, dt$CI.LB, dt$CI.UB))

dt2 <- pivot_wider(dt, id_cols = c("Param","id"),  names_from = c(Time), values_from = c(Recov,CI.LB,CI.UB,`Recovery (95% CI)`) )
dt2$`Recovery [95% CI]` <- ifelse(!is.na(dt2$`Recovery (95% CI)_Meta`), paste0("                 ",dt2$`Recovery (95% CI)_Meta`), paste0("T1 :", paste0(dt2$`Recovery (95% CI)_Time1`), " ; ", "T2: ", paste0(dt2$`Recovery (95% CI)_Time2`)))
dt2$` ` <- paste(rep(" ", 20), collapse = " ")


dt3<-dt2 %>% select(Param,Recov_Meta:11,15,16)

colnames(dt3)[colnames(dt3)=="Param"] <- "   "
colnames(dt3)[colnames(dt3)=="Recovery [95% CI]"] <- "    "

dt3$size_Meta <- ifelse(is.na(dt3$Recov_Meta), NA, 1.2)
dt3$size_T1 <- ifelse(is.na(dt3$Recov_Time1), NA, .6)
dt3$size_T2 <- ifelse(is.na(dt3$Recov_Time2), NA, .6)

# Set-up theme
tm <- forest_theme(
                   ci_pch = c(15, 16, 17),
                   ci_col = c("black","red","blue"),
                   ci_lty = c( "solid", "dashed", "dotted"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Time 1 ", "Time 2 "))

library(forestploter)
p <- forest(
  dt3[c(1,12,11)],
            est = list(dt3$Recov_Meta, dt3$Recov_Time1, dt3$Recov_Time2),
            lower = list(dt3$CI.LB_Meta, dt3$CI.LB_Time1, dt3$CI.LB_Time2), 
            upper = list(dt3$CI.UB_Meta, dt3$CI.UB_Time1, dt3$CI.UB_Time2),
            ci_column = 2,
            ref_line = .7,
            xlim = c(-.1, 1),
            ticks_at = c(0, .2, .5, .7, 1),
  sizes = list(dt3$size_Meta, dt3$size_T1, dt3$size_T2),
  theme = tm
)
plot(p)


library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt3),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt3),seq(from=1,to=nrow(dt3),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))
png("~/Desktop/PD_RecovForest.png", units="in", width=10, height=18, res=300)
plot(p)
dev.off()
null device 
          1 
# ggsave function
ggplot2::ggsave(paste0(saveto,"PD_RecovForest.png"),
                dpi = 300,
                width = 8, height = 17, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"PD_RecovForest.tiff"),
                dpi = 600,
                width = 8, height = 17, units = "in", plot=p)
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(ggplot2)
library(grid)
library(forestploter)
library(tidyverse)
```

```{r}
saveto <- "/Volumes/Research Project/IAT_Retest/Plotting/SavedFigs/"
```



```{r}
path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))
Cal1 <-read.csv("IndividualParametersQuadUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersQuadUCR2.csv", header = T)

colnames(Cal1)[2:ncol(Cal1)]<-paste(colnames(Cal1[2:ncol(Cal1)]),"T1",sep="_")
colnames(Cal2)[2:ncol(Cal2)]<-paste(colnames(Cal2[2:ncol(Cal2)]),"T2",sep="_")

Q.Cal <- merge(Cal1, Cal2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersQuadProjectImplicit1.csv")
PI2 <- read.csv("IndividualParametersQuadProjectImplicit2.csv")

colnames(PI1)[2:ncol(PI1)]<-paste(colnames(PI1[2:ncol(PI1)]),"T1",sep="_")
colnames(PI2)[2:ncol(PI2)]<-paste(colnames(PI2[2:ncol(PI2)]),"T2",sep="_")

Q.PI <- merge(PI1, PI2, by = "ID", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersQuadGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersQuadGawronski2.csv")

colnames(Gaw1)[2:ncol(Gaw1)]<-paste(colnames(Gaw1[2:ncol(Gaw1)]),"T1",sep="_")
colnames(Gaw2)[2:ncol(Gaw2)]<-paste(colnames(Gaw2[2:ncol(Gaw2)]),"T2",sep="_")

Q.Gaw <- merge(Gaw1, Gaw2, by = "code", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersQuadForscher1.csv", header = T)
For2<-read.csv("IndividualParametersQuadForscher2.csv", header = T)

colnames(For1)[2:ncol(For1)]<-paste(colnames(For1[2:ncol(For1)]),"T1",sep="_")
colnames(For2)[2:ncol(For2)]<-paste(colnames(For2[2:ncol(For2)]),"T2",sep="_")

Q.For <- merge(For1, For2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1Quad1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1Quad2.csv", header = T)

colnames(Lai11)[2:ncol(Lai11)]<-paste(colnames(Lai11[2:ncol(Lai11)]),"T1",sep="_")
colnames(Lai12)[2:ncol(Lai12)]<-paste(colnames(Lai12[2:ncol(Lai12)]),"T2",sep="_")

Q.Lai1 <- merge(Lai11, Lai12, by = "session_id", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2Quad1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2Quad2.csv", header = T)

colnames(Lai21)[2:ncol(Lai21)]<-paste(colnames(Lai21[2:ncol(Lai21)]),"T1",sep="_")
colnames(Lai22)[2:ncol(Lai22)]<-paste(colnames(Lai22[2:ncol(Lai22)]),"T2",sep="_")

Q.Lai2 <- merge(Lai21, Lai22, by = "session_id", all.x = T, all.y = T)

colnames(Q.Cal)[1] <- "subID"
Q.Cal <- cbind(Q.Cal, Study = "Calanchini")
colnames(Q.PI)[1] <- "subID"
Q.PI <- cbind(Q.PI, Study = "PI")
colnames(Q.Gaw)[1] <- "subID"
Q.Gaw <- cbind(Q.Gaw, Study = "Gawronski")
colnames(Q.For)[1] <- "subID"
Q.For <- cbind(Q.For, Study = "Forscher")
colnames(Q.Lai1)[1] <- "subID"
Q.Lai1 <- cbind(Q.Lai1, Study = "Lai 1")
colnames(Q.Lai2)[1] <- "subID"
Q.Lai2 <- cbind(Q.Lai2, Study = "Lai 2")

Quad <- rbind(Q.Cal,Q.PI,Q.Gaw,Q.For,Q.Lai1,Q.Lai2)


Quad$Study[Quad$Study=="PI"]<-"Project Implicit, 2020"
Quad$Study[Quad$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
Quad$Study[Quad$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
Quad$Study[Quad$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
Quad$Study[Quad$Study=="Gawronski"]<-"Gawronski et al., 2017"
Quad$Study[Quad$Study=="Forscher"]<-"Forscher et al., 2017"
```

```{r}
path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))
Cal1 <-read.csv("IndividualParametersPDUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersPDUCR2.csv", header = T)

colnames(Cal1)[2:ncol(Cal1)]<-paste(colnames(Cal1[2:ncol(Cal1)]),"T1",sep="_")
colnames(Cal2)[2:ncol(Cal2)]<-paste(colnames(Cal2[2:ncol(Cal2)]),"T2",sep="_")

P.Cal <- merge(Cal1, Cal2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersProjectImplicitPD1.csv")
PI2 <- read.csv("IndividualParametersProjectImplicitPD2.csv")

colnames(PI1)[2:ncol(PI1)]<-paste(colnames(PI1[2:ncol(PI1)]),"T1",sep="_")
colnames(PI2)[2:ncol(PI2)]<-paste(colnames(PI2[2:ncol(PI2)]),"T2",sep="_")

P.PI <- merge(PI1, PI2, by = "ID", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersPDGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersPDGawronski2.csv")

colnames(Gaw1)[2:ncol(Gaw1)]<-paste(colnames(Gaw1[2:ncol(Gaw1)]),"T1",sep="_")
colnames(Gaw2)[2:ncol(Gaw2)]<-paste(colnames(Gaw2[2:ncol(Gaw2)]),"T2",sep="_")

P.Gaw <- merge(Gaw1, Gaw2, by = "code", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersForscherPD1.csv", header = T)
For2<-read.csv("IndividualParametersForscherPD2.csv", header = T)

colnames(For1)[2:ncol(For1)]<-paste(colnames(For1[2:ncol(For1)]),"T1",sep="_")
colnames(For2)[2:ncol(For2)]<-paste(colnames(For2[2:ncol(For2)]),"T2",sep="_")

P.For <- merge(For1, For2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1PD1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1PD2.csv", header = T)

colnames(Lai11)[2:ncol(Lai11)]<-paste(colnames(Lai11[2:ncol(Lai11)]),"T1",sep="_")
colnames(Lai12)[2:ncol(Lai12)]<-paste(colnames(Lai12[2:ncol(Lai12)]),"T2",sep="_")

P.Lai1 <- merge(Lai11, Lai12, by = "session_id", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2PD1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2PD2.csv", header = T)

colnames(Lai21)[2:ncol(Lai21)]<-paste(colnames(Lai21[2:ncol(Lai21)]),"T1",sep="_")
colnames(Lai22)[2:ncol(Lai22)]<-paste(colnames(Lai22[2:ncol(Lai22)]),"T2",sep="_")

P.Lai2 <- merge(Lai21, Lai22, by = "session_id", all.x = T, all.y = T)

colnames(P.Cal)[1] <- "subID"
P.Cal <- cbind(P.Cal, Study = "Calanchini")
colnames(P.PI)[1] <- "subID"
P.PI <- cbind(P.PI, Study = "PI")
colnames(P.Gaw)[1] <- "subID"
P.Gaw <- cbind(P.Gaw, Study = "Gawronski")
colnames(P.For)[1] <- "subID"
P.For <- cbind(P.For, Study = "Forscher")
colnames(P.Lai1)[1] <- "subID"
P.Lai1 <- cbind(P.Lai1, Study = "Lai 1")
colnames(P.Lai2)[1] <- "subID"
P.Lai2 <- cbind(P.Lai2, Study = "Lai 2")

PD <- rbind(P.Cal,P.PI,P.Gaw,P.For,P.Lai1,P.Lai2)

PD$Study[PD$Study=="PI"]<-"Project Implicit, 2020"
PD$Study[PD$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
PD$Study[PD$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
PD$Study[PD$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
PD$Study[PD$Study=="Gawronski"]<-"Gawronski et al., 2017"
PD$Study[PD$Study=="Forscher"]<-"Forscher et al., 2017"
```

# Quad Model

## Associate Black-Bad

```{r}
ACbbScatter <- ggplot(Quad, aes(x=ACbb1_T1, y=ACbb1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association Black-Bad",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa() 
ACbbScatter
ggsave(paste0(saveto,"ACbbScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"ACbbScatter.tiff"),dpi=600)
```

## Associate White-Good

```{r}
ACwgScatter <- ggplot(Quad, aes(x=ACwg1_T1, y=ACwg1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association White-Good",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) + theme(legend.title = element_text(size = 9)) +
  jtools::theme_apa()
ACwgScatter
ggsave(paste0(saveto,"ACwgScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"ACwgScatter.tiff"),dpi=600)
```

## Detection

```{r}
DScatter <- ggplot(Quad, aes(x=D1_T1, y=D1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Detection",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) + theme(legend.title = element_text(size = 9)) +
  jtools::theme_apa()
DScatter
ggsave(paste0(saveto,"DScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"DScatter.tiff"),dpi=600)
```

## Overcoming Bias

```{r}
OBScatter <- ggplot(Quad, aes(x=OB1_T1, y=OB1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Overcoming Bias",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) + theme(legend.title = element_text(size = 9))  +
  jtools::theme_apa()
OBScatter
ggsave(paste0(saveto,"OBScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"OBScatter.tiff"),dpi=600)
```

## Guessing

```{r}
GScatter <- ggplot(Quad, aes(x=G1_T1, y=G1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Guessing",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
ggsave(paste0(saveto,"GScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"GScatter.tiff"),dpi=600)
```

## Panel

```{r}
library(ggpubr)
QuadPanel1 <- ggarrange(ACwgScatter, ACbbScatter, DScatter, OBScatter, GScatter,
          ncol = 3, nrow = 2, common.legend = T)
QuadPanel1

require(grid)   # for the textGrob() function

QuadPanel2 <- ggarrange(ACwgScatter  + theme(plot.title = element_text(size=12)) + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        ACbbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        DScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        OBScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        GScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = "bottom",
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))
QuadPanel2

annotate_figure(QuadPanel2, left = textGrob("Time 2", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Time 1", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
ggsave(paste0(saveto,"QuadPanel.jpg"),dpi=600)
ggsave(paste0(saveto,"QuadPanel.tiff"),dpi=600)
```


# PD Model

## Automatic Black

```{r}
AbScatter <- ggplot(PD, aes(x=Ab1_T1, y=Ab1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic Black",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
AbScatter
ggsave(paste0(saveto,"AbScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"AbScatter.tiff"),dpi=600)
```

## Associate White

```{r}
AwScatter <- ggplot(PD, aes(x=Aw1_T1, y=Aw1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic White",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
AwScatter
ggsave(paste0(saveto,"AwScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"AwScatter.tiff"),dpi=600)
```

## Control Good

```{r}
CpScatter <- ggplot(PD, aes(x=Cp1_T1, y=Cp1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Good",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CpScatter
ggsave(paste0(saveto,"CpScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"CpScatter.tiff"),dpi=600)
```

## Control Bad

```{r}
CuScatter <- ggplot(PD, aes(x=Cu1_T1, y=Cu1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Bad",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CuScatter
ggsave(paste0(saveto,"CuScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"CuScatter.tiff"),dpi=600)
```

## Control White

```{r}
CwScatter <- ggplot(PD, aes(x=Cw1_T1, y=Cw1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control White",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CwScatter
ggsave(paste0(saveto,"CwScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"CwScatter.tiff"),dpi=600)
```

## Control Black

```{r}
CbScatter <- ggplot(PD, aes(x=Cb1_T1, y=Cb1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Black",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CbScatter
ggsave(paste0(saveto,"CbScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"CbScatter.tiff"),dpi=600)
```

## Panel

```{r}
library(ggpubr)
PDPanel1 <- ggarrange(AwScatter, AbScatter, CuScatter, CpScatter, CbScatter, CwScatter,
          ncol = 3, nrow = 2, common.legend = T)
PDPanel1

require(grid)   # for the textGrob() function

PDPanel2 <- ggarrange(AwScatter  + theme(plot.title = element_text(size=12))  + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        AbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CuScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CpScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CwScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = "bottom",
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))
PDPanel2 <- annotate_figure(PDPanel2, left = textGrob("Time 2", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Time 1", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
PDPanel2
ggsave(paste0(saveto,"PDPanel.jpg"),dpi=600)
ggsave(paste0(saveto,"PDPanel.tiff"),dpi=600)
```

# Reliability Meta-Analysis

```{r}
dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Retest/output/combQuad_Df.csv")[2:6]

dt$Param[dt$Param=="D"]<-"Detection"
dt$Param[dt$Param=="OB"]<-"Overcoming Bias"
dt$Param[dt$Param=="ACbb"]<-"Association Black-Bad"
dt$Param[dt$Param=="ACwg"]<-"Association White-Good"
dt$Param[dt$Param=="G"]<-"Guessing"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

colnames(dt)[colnames(dt)=="Param"] <- "   "
dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`ICC (95% CI)` <- ifelse(is.na(dt$ICC), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$ICC, dt$CI.LB, dt$CI.UB))


dt$ICCstudy <- ifelse(dt$type==1, dt$ICC, NA)
dt$ICC.LBstudy <- ifelse(dt$type==1, dt$CI.LB, NA)
dt$ICC.UBstudy <- ifelse(dt$type==1, dt$CI.UB, NA)
dt$Sizestudy <- ifelse(dt$type==1, .6, NA)
dt$Sizemeta <- ifelse(dt$type==0, 1.2, NA)
dt$ICCmeta <- ifelse(dt$type==0, dt$ICC, NA)
dt$ICC.LBmeta <- ifelse(dt$type==0, dt$CI.LB, NA)
dt$ICC.UBmeta <- ifelse(dt$type==0, dt$CI.UB, NA)

#dt<-dt[c(1:4,6:7)]
dt<-dt[c(1:4,6:ncol(dt))]
colnames(dt)[colnames(dt)=="ICC (95% CI)"] <- "     "

library(forestploter)
# p <- forest(
#   dt[c(1,5,6)],
#             est = dt$ICC,
#             lower = dt$CI.LB, 
#             upper = dt$CI.UB,
#             ci_column = 2,
#             ref_line = .4,
#             xlim = c(-.1, 1),
#             ticks_at = c(0, .4,.6, .75, 1),
#   sizes = .1
# )

tm <- forest_theme(
                   ci_pch = c(15, 16),
                   ci_col = c("black","red"),
                   ci_lty = c( "solid", "dashed"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Study")
                  
                  )

p <- forest(
  dt[c(1,5,6)],
            est = list(dt$ICCmeta, dt$ICCstudy),
            lower = list(dt$ICC.LBmeta, dt$ICC.LBstudy), 
            upper = list(dt$ICC.UBmeta, dt$ICC.UBstudy),
              ci_column = 2,
            ref_line = .4,
            xlim = c(-.1, 1),
            ticks_at = c(0, .4,.6, .75, 1),
  sizes = list(dt$Sizemeta, dt$Sizestudy),
  theme = tm
)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt),seq(from=1,to=nrow(dt),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))

```



```{r}
png(paste0(saveto,"Quad_TRT.png"), units="in", width=10, height=12, res=300)
plot(p)
dev.off()
```


```{r}
# ggsave function
ggplot2::ggsave(paste0(saveto,"Quad_TRT.png"),
                dpi = 300,
                width = 6, height = 11, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"Quad_TRT.tiff"),
                dpi = 600,
                width = 6, height = 11, units = "in", plot=p)
```

```{r}
dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Retest/output/combPD_Df.csv")[2:6]

dt$Param[dt$Param=="Ab"]<-"Automatic Black"
dt$Param[dt$Param=="Aw"]<-"Automatic White"
dt$Param[dt$Param=="Cb"]<-"Control Black"
dt$Param[dt$Param=="Cw"]<-"Control White"
dt$Param[dt$Param=="Cu"]<-"Control Bad"
dt$Param[dt$Param=="Cp"]<-"Control Good"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

colnames(dt)[colnames(dt)=="Param"] <- "   "
colnames(dt)[colnames(dt)=="Param"] <- "   "
dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`ICC (95% CI)` <- ifelse(is.na(dt$ICC), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$ICC, dt$CI.LB, dt$CI.UB))

dt$ICCstudy <- ifelse(dt$type==1, dt$ICC, NA)
dt$ICC.LBstudy <- ifelse(dt$type==1, dt$CI.LB, NA)
dt$ICC.UBstudy <- ifelse(dt$type==1, dt$CI.UB, NA)
dt$Sizestudy <- ifelse(dt$type==1, .6, NA)
dt$Sizemeta <- ifelse(dt$type==0, 1.2, NA)
dt$ICCmeta <- ifelse(dt$type==0, dt$ICC, NA)
dt$ICC.LBmeta <- ifelse(dt$type==0, dt$CI.LB, NA)
dt$ICC.UBmeta <- ifelse(dt$type==0, dt$CI.UB, NA)

#dt<-dt[c(1:4,6:7)]
dt<-dt[c(1:4,6:ncol(dt))]
colnames(dt)[colnames(dt)=="ICC (95% CI)"] <- "     "

library(forestploter)
# p <- forest(
#   dt[c(1,5,6)],
#             est = dt$ICC,
#             lower = dt$CI.LB, 
#             upper = dt$CI.UB,
#             ci_column = 2,
#             ref_line = .4,
#             xlim = c(-.1, 1),
#             ticks_at = c(0, .4,.6, .75, 1),
#   sizes = .1
# )

tm <- forest_theme(
                   ci_pch = c(15, 16),
                   ci_col = c("black","red"),
                   ci_lty = c( "solid", "dashed"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Study")
                  
                  )

p <- forest(
  dt[c(1,5,6)],
            est = list(dt$ICCmeta, dt$ICCstudy),
            lower = list(dt$ICC.LBmeta, dt$ICC.LBstudy), 
            upper = list(dt$ICC.UBmeta, dt$ICC.UBstudy),
              ci_column = 2,
            ref_line = .4,
            xlim = c(-.1, 1),
            ticks_at = c(0, .4,.6, .75, 1),
  sizes = list(dt$Sizemeta, dt$Sizestudy),
  theme = tm
)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt),seq(from=1,to=nrow(dt),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))

```



```{r}
png(paste0(saveto,"PDP_TRT.png"), units="in", width=10, height=15, res=300)
plot(p)
dev.off()
```

```{r}
# ggsave function
ggplot2::ggsave(paste0(saveto,"PDP_TRT.png"),
                dpi = 300,
                width = 6, height = 13, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"PDP_TRT.tiff"),
                dpi = 600,
                width = 6, height = 13, units = "in", plot=p)
```


# Recovery

```{r}
setwd(paste0("/Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/PD/"))
times<-c("Time1","Time2")

allrecovs <- as.data.frame(matrix(ncol=14,nrow=0))

PDdirs <- list.dirs(path = ".", full.names = TRUE, recursive = TRUE)[-1]
for(d in PDdirs){
  for(ti in times){
    curFiles <- list.files(path=d,pattern="\\.csv$")
    id<-grep(ti,curFiles)
    output<-read.csv(paste0(d,"/",curFiles[id]))
    name<-gsub("./","",d)
    name<-gsub(" ","",name)
    
    if(ti=="Time1"){
      #colnames(output) <- paste(colnames(output),"T1",sep="_")
      output$time <- "T1"
    }else if(ti=="Time2"){
      #colnames(output) <- paste(colnames(output),"T2",sep="_")
      output$time <- "T2"
    }
    
    assign(paste0(name,".PD.Recov.",ti),output)
  }
  df <- rbind(get(paste0(name,".PD.Recov.Time1")),get(paste0(name,".PD.Recov.Time2")))
  assign(paste0(name,".PD.Recov"),df)
  allrecovs <- rbind(allrecovs,cbind(df,Study=name))
}


allrecovs$Study <- gsub("Lai1", "Lai 1", allrecovs$Study)
allrecovs$Study <- gsub("Lai2", "Lai 2", allrecovs$Study)
PD.recovs <- allrecovs
PD.recovs <- dplyr::rename(PD.recovs, subID=X)

tapply(PD.recovs$Ab1, PD.recovs$Study, function(x) length(x))
```

```{r}
path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))
Cal1 <-read.csv("IndividualParametersPDUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersPDUCR2.csv", header = T)

Cal1$time <- "T1"
Cal2$time <- "T2"
Cal1$subID <- 1:nrow(Cal1)
Cal2$subID <- 1:nrow(Cal2)
P.Cal <- rbind(Cal1, Cal2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersProjectImplicitPD1.csv")
PI2 <- read.csv("IndividualParametersProjectImplicitPD2.csv")

PI1$time <- "T1"
PI2$time <- "T2"
PI1$subID <- 1:nrow(PI1)
PI2$subID <- 1:nrow(PI2)
P.PI <- rbind(PI1, PI2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersPDGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersPDGawronski2.csv")

Gaw1$time <- "T1"
Gaw2$time <- "T2"
Gaw1$subID <- 1:nrow(Gaw1)
Gaw2$subID <- 1:nrow(Gaw2)
P.Gaw <- rbind(Gaw1, Gaw2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersForscherPD1.csv", header = T)
For2<-read.csv("IndividualParametersForscherPD2.csv", header = T)

For1$time <- "T1"
For2$time <- "T2"
For1$subID <- 1:nrow(For1)
For2$subID <- 1:nrow(For2)
P.For <- rbind(For1, For2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1PD1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1PD2.csv", header = T)

Lai11$time <- "T1"
Lai12$time <- "T2"
Lai11$subID <- 1:nrow(Lai11)
Lai12$subID <- 1:nrow(Lai12)
P.Lai1 <- rbind(Lai11, Lai12)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2PD1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2PD2.csv", header = T)

Lai21$time <- "T1"
Lai22$time <- "T2"
Lai21$subID <- 1:nrow(Lai21)
Lai22$subID <- 1:nrow(Lai22)
P.Lai2 <- rbind(Lai21,Lai22)

colnames(P.Cal)[1] <- "subID"
P.Cal <- cbind(P.Cal, Study = "Calanchini")
colnames(P.PI)[1] <- "subID"
P.PI <- cbind(P.PI, Study = "PI")
colnames(P.Gaw)[1] <- "subID"
P.Gaw <- cbind(P.Gaw, Study = "Gawronski")
colnames(P.For)[1] <- "subID"
P.For <- cbind(P.For, Study = "Forscher")
colnames(P.Lai1)[1] <- "subID"
P.Lai1 <- cbind(P.Lai1, Study = "Lai 1")
colnames(P.Lai2)[1] <- "subID"
P.Lai2 <- cbind(P.Lai2, Study = "Lai 2")

PD.orig <- rbind(P.Cal,P.PI,P.Gaw,P.For,P.Lai1,P.Lai2)
PD.orig[1] <- NULL
```

```{r}

colnames(PD.recovs)[2: (ncol(PD.recovs)-2) ] <- paste(colnames(PD.recovs)[2: (ncol(PD.recovs)-2) ],"R",sep="_")
colnames(PD.orig)[1: (ncol(PD.orig)-3) ] <- paste(colnames(PD.orig)[1: (ncol(PD.orig)-3) ],"O",sep="_")
PD.orig <- PD.orig[order(PD.orig$Study, PD.orig$time),]
PD.recovs <- PD.recovs[order(PD.recovs$Study, PD.recovs$time),]

#PD.orig.recovs <- cbind(PD.orig,PD.recovs)
PD.orig.recovs <- merge(PD.orig, PD.recovs, by = c("subID","Study","time"))

PD.orig.recovs$Study[PD.orig.recovs$Study=="PI"]<-"Project Implicit, 2020"
PD.orig.recovs$Study[PD.orig.recovs$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
PD.orig.recovs$Study[PD.orig.recovs$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
PD.orig.recovs$Study[PD.orig.recovs$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
PD.orig.recovs$Study[PD.orig.recovs$Study=="Gawronski"]<-"Gawronski et al., 2017"
PD.orig.recovs$Study[PD.orig.recovs$Study=="Forscher"]<-"Forscher et al., 2017"
```


# Recovery

## PD Model

### Automatic Black

```{r}
AbScatter <- ggplot(PD.orig.recovs, aes(x=Ab1_O, y=Ab1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype = "dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic Black",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()

AbScatter
ggsave(paste0(saveto,"AbScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"AbScatterREC.tiff"),dpi=600)
```

### Automatic White

```{r}
AwScatter <- ggplot(PD.orig.recovs, aes(x=Aw1_O, y=Aw1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic White",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
AwScatter
ggsave(paste0(saveto,"AwScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"AwScatterREC.tiff"),dpi=600)
```

### Control Good

```{r}
CpScatter <- ggplot(PD.orig.recovs, aes(x=Cp1_O, y=Cp1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Good",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CpScatter
ggsave(paste0(saveto,"CpScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"AwScatterREC.tiff"),dpi=600)
```

### Control Bad

```{r}
CuScatter <- ggplot(PD.orig.recovs, aes(x=Cu1_O, y=Cu1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Bad",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CuScatter
ggsave(paste0(saveto,"CuScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"CuScatterREC.tiff"),dpi=600)
```

### Control White

```{r}
CwScatter <- ggplot(PD.orig.recovs, aes(x=Cw1_O, y=Cw1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control White",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CwScatter
ggsave(paste0(saveto,"CwScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"CwScatterREC.tiff"),dpi=600)
```

### Control Black

```{r}
CbScatter <- ggplot(PD.orig.recovs, aes(x=Cb1_O, y=Cb1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Black",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CbScatter
ggsave(paste0(saveto,"CbScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"CbScatterREC.tiff"),dpi=600)
```

### Panel

```{r}
library(ggpubr)
PDPanel1 <- ggarrange(AwScatter, AbScatter, CuScatter, CpScatter, CbScatter, CwScatter,
          ncol = 3, nrow = 2, common.legend = T)
PDPanel1

require(grid)   # for the textGrob() function

PDPanel2 <- ggarrange(AwScatter  + theme(plot.title = element_text(size=12)) + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        AbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CuScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CpScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CwScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = c("bottom"),
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))

PDPanel2 <- annotate_figure(PDPanel2, left = textGrob("Recovered", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Original", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
PDPanel2
ggsave(paste0(saveto,"PDPanelREC.jpg"),dpi=600)
ggsave(paste0(saveto,"PDPanelREC.tiff"),dpi=600)
```

## Quad Model

# Recovery

```{r}
setwd(paste0("/Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/Quad/"))
times<-c("Time1","Time2")

allrecovs <- as.data.frame(matrix(ncol=14,nrow=0))

Quaddirs <- list.dirs(path = ".", full.names = TRUE, recursive = TRUE)[-1]
for(d in Quaddirs){
  for(ti in times){
    curFiles <- list.files(path=d,pattern="\\.csv$")
    id<-grep(ti,curFiles)
    output<-read.csv(paste0(d,"/",curFiles[id]))
    name<-gsub("./","",d)
    name<-gsub(" ","",name)
    
    if(ti=="Time1"){
      #colnames(output) <- paste(colnames(output),"T1",sep="_")
      output$time <- "T1"
    }else if(ti=="Time2"){
      #colnames(output) <- paste(colnames(output),"T2",sep="_")
      output$time <- "T2"
    }
    
    assign(paste0(name,".Quad.Recov.",ti),output)
  }
  df <- rbind(get(paste0(name,".Quad.Recov.Time1")),get(paste0(name,".Quad.Recov.Time2")))
  assign(paste0(name,".Quad.Recov"),df)
  allrecovs <- rbind(allrecovs,cbind(df,Study=name))
}


allrecovs$Study <- gsub("Lai1", "Lai 1", allrecovs$Study)
allrecovs$Study <- gsub("Lai2", "Lai 2", allrecovs$Study)
Quad.recovs <- allrecovs
Quad.recovs <- dplyr::rename(Quad.recovs, subID=X)

tapply(Quad.recovs$ACbb1, Quad.recovs$Study, function(x) length(x))
```

```{r}
path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))
Cal1 <-read.csv("IndividualParametersQuadUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersQuadUCR2.csv", header = T)

Cal1$time <- "T1"
Cal2$time <- "T2"
Cal1$subID <- 1:nrow(Cal1)
Cal2$subID <- 1:nrow(Cal2)
P.Cal <- rbind(Cal1, Cal2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersQuadProjectImplicit1.csv")
PI2 <- read.csv("IndividualParametersQuadProjectImplicit2.csv")

PI1$time <- "T1"
PI2$time <- "T2"
PI1$subID <- 1:nrow(PI1)
PI2$subID <- 1:nrow(PI2)
P.PI <- rbind(PI1, PI2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersQuadGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersQuadGawronski2.csv")

Gaw1$time <- "T1"
Gaw2$time <- "T2"
Gaw1$subID <- 1:nrow(Gaw1)
Gaw2$subID <- 1:nrow(Gaw2)
P.Gaw <- rbind(Gaw1, Gaw2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersQuadForscher1.csv", header = T)
For2<-read.csv("IndividualParametersQuadForscher2.csv", header = T)

For1$time <- "T1"
For2$time <- "T2"
For1$subID <- 1:nrow(For1)
For2$subID <- 1:nrow(For2)
P.For <- rbind(For1, For2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1Quad1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1Quad2.csv", header = T)

Lai11$time <- "T1"
Lai12$time <- "T2"
Lai11$subID <- 1:nrow(Lai11)
Lai12$subID <- 1:nrow(Lai12)
P.Lai1 <- rbind(Lai11, Lai12)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2Quad1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2Quad2.csv", header = T)

Lai21$time <- "T1"
Lai22$time <- "T2"
Lai21$subID <- 1:nrow(Lai21)
Lai22$subID <- 1:nrow(Lai22)
P.Lai2 <- rbind(Lai21,Lai22)

colnames(P.Cal)[1] <- "subID"
P.Cal <- cbind(P.Cal, Study = "Calanchini")
colnames(P.PI)[1] <- "subID"
P.PI <- cbind(P.PI, Study = "PI")
colnames(P.Gaw)[1] <- "subID"
P.Gaw <- cbind(P.Gaw, Study = "Gawronski")
colnames(P.For)[1] <- "subID"
P.For <- cbind(P.For, Study = "Forscher")
colnames(P.Lai1)[1] <- "subID"
P.Lai1 <- cbind(P.Lai1, Study = "Lai 1")
colnames(P.Lai2)[1] <- "subID"
P.Lai2 <- cbind(P.Lai2, Study = "Lai 2")

Quad.orig <- rbind(P.Cal,P.PI,P.Gaw,P.For,P.Lai1,P.Lai2)
Quad.orig[1] <- NULL
```

```{r}
colnames(Quad.recovs)[2: (ncol(Quad.recovs)-2) ] <- paste(colnames(Quad.recovs)[2: (ncol(Quad.recovs)-2) ],"R",sep="_")
colnames(Quad.orig)[1: (ncol(Quad.orig)-3) ] <- paste(colnames(Quad.orig)[1: (ncol(Quad.orig)-3) ],"O",sep="_")
Quad.orig <- Quad.orig[order(Quad.orig$Study, Quad.orig$time),]
Quad.recovs <- Quad.recovs[order(Quad.recovs$Study, Quad.recovs$time),]

#Quad.orig.recovs <- cbind(Quad.orig,Quad.recovs)
Quad.orig.recovs <- merge(Quad.orig, Quad.recovs, by = c("subID","Study","time"))

Quad.orig.recovs$Study[Quad.orig.recovs$Study=="PI"]<-"Project Implicit, 2020"
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Gawronski"]<-"Gawronski et al., 2017"
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Forscher"]<-"Forscher et al., 2017"
```

## Quad Model

### Associate Black-Bad

```{r}
ACbbScatter <- ggplot(Quad.orig.recovs, aes(x=ACbb1_O, y=ACbb1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association Black-Bad",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
ACbbScatter
ggsave(paste0(saveto,"ACbbScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"ACbbScatterREC.tiff"),dpi=600)
```

### Associate White-Good

```{r}
ACwgScatter <- ggplot(Quad.orig.recovs, aes(x=ACwg1_O, y=ACwg1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association White-Good",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
ACwgScatter
ggsave(paste0(saveto,"ACwgScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"ACwgScatterREC.tiff"),dpi=600)
```

### Detection

```{r}
DScatter <- ggplot(Quad.orig.recovs, aes(x=D1_O, y=D1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Detection",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
DScatter
ggsave(paste0(saveto,"DScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"DScatterREC.tiff"),dpi=600)
```

### Overcoming Bias

```{r}
OBScatter <- ggplot(Quad.orig.recovs, aes(x=OB1_O, y=OB1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Overcoming Bias",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
OBScatter
ggsave(paste0(saveto,"OBScatterREC.jpg"),dpi=600)
ggsave(paste0(saveto,"OBScatterREC.tiff"),dpi=600)
```

### Guessing

```{r}
GScatter <- ggplot(Quad.orig.recovs, aes(x=G1_O, y=G1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Guessing",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
GScatter
ggsave(paste0(saveto,"GScatter.jpg"),dpi=600)
ggsave(paste0(saveto,"GScatter.tiff"),dpi=600)
```

### Panel

```{r}
library(ggpubr)
QuadPanel1 <- ggarrange(ACwgScatter, ACbbScatter, DScatter, OBScatter, GScatter,
          ncol = 3, nrow = 2, common.legend = T)
QuadPanel1

require(grid)   # for the textGrob() function

QuadPanel2 <- ggarrange(ACwgScatter  + theme(plot.title = element_text(size=12))  + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        ACbbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        DScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        OBScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        GScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = "bottom",
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))
QuadPanel2

annotate_figure(QuadPanel2, left = textGrob("Recovered", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Original", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
ggsave(paste0(saveto,"QuadPanelREC.jpg"),dpi=600)
ggsave(paste0(saveto,"QuadPanelREC.tiff"),dpi=600)
```

# Recovery Meta-Analysis

## Quad


```{r}
dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Recovery/output/combQuad_Recovery_Df.csv")[2:7]
dt$id <- unlist(lapply(1:5, function(x) rep(x,times=13)))

dt$Param[dt$Param=="D"]<-"Detection"
dt$Param[dt$Param=="OB"]<-"Overcoming Bias"
dt$Param[dt$Param=="ACbb"]<-"Association Black-Bad"
dt$Param[dt$Param=="ACwg"]<-"Association White-Good"
dt$Param[dt$Param=="G"]<-"Guessing"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`Recovery (95% CI)` <- ifelse(is.na(dt$Recov), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$Recov, dt$CI.LB, dt$CI.UB))

dt2 <- pivot_wider(dt, id_cols = c("Param","id"),  names_from = c(Time), values_from = c(Recov,CI.LB,CI.UB,`Recovery (95% CI)`) )
dt2$`Recovery [95% CI]` <- ifelse(!is.na(dt2$`Recovery (95% CI)_Meta`), paste0("                 ",dt2$`Recovery (95% CI)_Meta`), paste0("T1 :", paste0(dt2$`Recovery (95% CI)_Time1`), " ; ", "T2: ", paste0(dt2$`Recovery (95% CI)_Time2`)))
dt2$` ` <- paste(rep(" ", 20), collapse = " ")


dt3<-dt2 %>% select(Param,Recov_Meta:11,15,16)

colnames(dt3)[colnames(dt3)=="Param"] <- "   "
colnames(dt3)[colnames(dt3)=="Recovery [95% CI]"] <- "    "

dt3$size_Meta <- ifelse(is.na(dt3$Recov_Meta), NA, 1.2)
dt3$size_T1 <- ifelse(is.na(dt3$Recov_Time1), NA, .6)
dt3$size_T2 <- ifelse(is.na(dt3$Recov_Time2), NA, .6)

# Set-up theme
tm <- forest_theme(
                   ci_pch = c(15, 16, 17),
                   ci_col = c("black","red","blue"),
                   ci_lty = c( "solid", "dashed", "dotted"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Time 1 ", "Time 2 "))

library(forestploter)
p <- forest(
  dt3[c(1,12,11)],
            est = list(dt3$Recov_Meta, dt3$Recov_Time1, dt3$Recov_Time2),
            lower = list(dt3$CI.LB_Meta, dt3$CI.LB_Time1, dt3$CI.LB_Time2), 
            upper = list(dt3$CI.UB_Meta, dt3$CI.UB_Time1, dt3$CI.UB_Time2),
            ci_column = 2,
            ref_line = .7,
            xlim = c(-.1, 1),
            ticks_at = c(0, .2, .5, .7, 1),
  sizes = list(dt3$size_Meta, dt3$size_T1, dt3$size_T2),
  theme = tm
)
plot(p)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt3),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt3),seq(from=1,to=nrow(dt3),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))

```

```{r}
png("~/Desktop/Quad_RecovForest.png", units="in", width=10, height=15, res=300)
plot(p)
dev.off()
```

```{r}
# ggsave function
ggplot2::ggsave(paste0(saveto,"Quad_RecovForest.png"),
                dpi = 300,
                width = 8, height = 15, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"Quad_RecovForest.tiff"),
                dpi = 300,
                width = 8, height = 15, units = "in", plot=p)
```


```{r}
dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Recovery/output/combPD_Recovery_Df.csv")[2:7]
dt$id <- unlist(lapply(1:6, function(x) rep(x,times=13)))

dt$Param[dt$Param=="Ab"]<-"Automatic Black"
dt$Param[dt$Param=="Aw"]<-"Automatic White"
dt$Param[dt$Param=="Cb"]<-"Control Black"
dt$Param[dt$Param=="Cw"]<-"Control White"
dt$Param[dt$Param=="Cu"]<-"Control Bad"
dt$Param[dt$Param=="Cp"]<-"Control Good"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`Recovery (95% CI)` <- ifelse(is.na(dt$Recov), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$Recov, dt$CI.LB, dt$CI.UB))

dt2 <- pivot_wider(dt, id_cols = c("Param","id"),  names_from = c(Time), values_from = c(Recov,CI.LB,CI.UB,`Recovery (95% CI)`) )
dt2$`Recovery [95% CI]` <- ifelse(!is.na(dt2$`Recovery (95% CI)_Meta`), paste0("                 ",dt2$`Recovery (95% CI)_Meta`), paste0("T1 :", paste0(dt2$`Recovery (95% CI)_Time1`), " ; ", "T2: ", paste0(dt2$`Recovery (95% CI)_Time2`)))
dt2$` ` <- paste(rep(" ", 20), collapse = " ")


dt3<-dt2 %>% select(Param,Recov_Meta:11,15,16)

colnames(dt3)[colnames(dt3)=="Param"] <- "   "
colnames(dt3)[colnames(dt3)=="Recovery [95% CI]"] <- "    "

dt3$size_Meta <- ifelse(is.na(dt3$Recov_Meta), NA, 1.2)
dt3$size_T1 <- ifelse(is.na(dt3$Recov_Time1), NA, .6)
dt3$size_T2 <- ifelse(is.na(dt3$Recov_Time2), NA, .6)

# Set-up theme
tm <- forest_theme(
                   ci_pch = c(15, 16, 17),
                   ci_col = c("black","red","blue"),
                   ci_lty = c( "solid", "dashed", "dotted"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Time 1 ", "Time 2 "))

library(forestploter)
p <- forest(
  dt3[c(1,12,11)],
            est = list(dt3$Recov_Meta, dt3$Recov_Time1, dt3$Recov_Time2),
            lower = list(dt3$CI.LB_Meta, dt3$CI.LB_Time1, dt3$CI.LB_Time2), 
            upper = list(dt3$CI.UB_Meta, dt3$CI.UB_Time1, dt3$CI.UB_Time2),
            ci_column = 2,
            ref_line = .7,
            xlim = c(-.1, 1),
            ticks_at = c(0, .2, .5, .7, 1),
  sizes = list(dt3$size_Meta, dt3$size_T1, dt3$size_T2),
  theme = tm
)
plot(p)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt3),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt3),seq(from=1,to=nrow(dt3),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))

```

```{r}
png("~/Desktop/PD_RecovForest.png", units="in", width=10, height=18, res=300)
plot(p)
dev.off()
```

```{r}
# ggsave function
ggplot2::ggsave(paste0(saveto,"PD_RecovForest.png"),
                dpi = 300,
                width = 8, height = 17, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"PD_RecovForest.tiff"),
                dpi = 600,
                width = 8, height = 17, units = "in", plot=p)
```